Abstract
The integration of artificial intelligence (AI) in healthcare presents a dual challenge: maximizing the efficiency of medical processes while safeguarding patient privacy. This comprehensive review examines the delicate balance between leveraging AI's potential in healthcare and preserving individual data privacy. Through analysis of recent literature, case studies, and regulatory frameworks, we explore the current landscape of AI applications in healthcare, associated privacy risks, and emerging solutions. Findings reveal that while AI significantly enhances diagnostic accuracy and treatment planning, it also raises concerns about data security and patient confidentiality. Key challenges include ensuring GDPR and HIPAA compliance, managing large-scale health data, and maintaining transparency in AI decision-making processes. Promising approaches such as federated learning and differential privacy emerge as potential solutions. This review underscores the need for a multidisciplinary approach involving healthcare providers, AI developers, ethicists, and policymakers to create robust, privacy-preserving AI systems in healthcare.
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